DESCRIPTION (from abstract): A thorough understanding of visual information processing is important not only because vision is the largest source of sensory input to the nervous system, but also because the visual system is a model for neural information processing in general. The proposed research continues our attempts to advance the understanding of complex visual processing, with an emphasis on the relationship of local feature extraction to the longer-range processes that are involved in image segmentation and object recognition. The project has three goals: to determine the dynamics of the computations involved in vernier acuity, to determine the dynamics of the computations involved in illusory contour formation, and to understand their mechanism and relationship. For both vernier acuity and illusory contour formation, we make use of a framework dynamical model. This model guides the exploration of critical stimulus parameters (such as temporal frequency and temporal phase of stimulus components) and provides a rigorous way to compare the dynamics of the two processes. We anticipate that these dynamics will be rather different, and the later experiments will isolate various spatial aspects of the stimuli, in order to understand the basis for this difference. Vernier acuity and illusory contour formation both depend critically on stimulus geometry. Current psychophysical evidence is relatively limited, but suggests that only the short-range hyperacuity phenomena depend critically on stimulus timing. This is, at least superficially, at variance with the increasingly popular notion that some kind of neural synchronization, in addition to traditional nonlinearities, form the physiological basis for the perceptual binding of components of a stimulus into a single object. Our approach, which combines psychophysics, visual evoked potentials, and modeling, will delineate both traditional nonlinearities and, should they exist, stimulus-dependent oscillations and related phenomena. This detailed analysis of the dynamics of neural computations is likely to advance our understanding of how visual perception occurs.